Overview of Heating Control Model in Coking Production

Overview of Coking Heating Control Model Coking is one of the most important processes in petrochemical production and an advanced technology used in the production of carbon materials. In the process of coking, a key step is the heating control of the coking furnace, which plays an important rol......

Overview of Coking Heating Control Model

Coking is one of the most important processes in petrochemical production and an advanced technology used in the production of carbon materials. In the process of coking, a key step is the heating control of the coking furnace, which plays an important role in product quality, energy saving and raw material utilization. Therefore, how to improve the heating control of coking furnaces becomes an important issue.

Various heating control systems for coking furnaces have been developed and applied in the petroleum and petrochemical industries, including programmable logic controllers (PLCs) and distributed control systems (DCS). However, these traditional systems have some limitations, such as the inability to efficiently coordinate multiple heating systems, difficulty in calculating optimal control strategies, and the lack of fault-tolerant ability. In order to solve these problems, researchers have proposed various advanced control strategies such as model predictive control (MPC).

MPC is a predictive control method which uses dynamic models of the process to predict future behavior. The MPC can approximate optimal control strategies for complex nonlinear processes, thus improving the accuracy of the control. On the basis of the conventional PID control and on-off switching control, the MPC is applied in the heating control of the coking furnace to realize the reasonable coordination of multiple heating systems and the perfect combination of energy saving, product quality, raw material utilization and safety control.

Firstly, the dynamic model of coking process is established and applied in the control system of coking furnace heating. Through the dynamic model of coking process, the operator can construct the reference trajectory of the coking furnace temperature, as well as corresponding constraints.

Secondly, a model predictive control algorithm is introduced to optimize the control parameters of the coking furnace heating. According to the reference trajectory and other constraints, the control algorithm calculates the best control strategy to achieve the target.

Thirdly, a fault detection and fault-tolerant control system can be set up to effectively detect and solve the fault, so that the whole system can operate safely and stably.

Finally, a simulation system is built to validate the performance of the coking furnace heating control system. Through the simulation comparison, the performance of the coking furnace heating control system with and without MPC can be evaluated.

In summary, this paper introduces the coking heating control model, which can optimize the control/operation policy of coking furnaces. The application of the model can effectively improve the accuracy and speed of the control, as well as safety and reliability of the control system. At the same time, the development of coking heating control models also has potential applications in the field of energysaving and emission reduction.

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